Additional file 1 of Association between periodontitis and uric acid levels in blood and oral fluids: a systematic review and meta-analysis
Bibliographic record
Abstract
Additional file 1: Table S1. The characteristics of excluded studies. Table S2. Quality assessment of the included case-control studies with Newcastle-Ottawa Scale. Table S3. Agency for Healthcare Research and Quality for risk of bias assessment of the cross-sectional studies. Figure S1. Sensitivity analysis of the relationship between periodontitis and controls of UA levels in blood. In these studies, no clearly heterogeneous origin could be found. Figure S2. Forest plot comparing UA levels of periodontitis vs. control in plasma/serum subgroups. CI, confidence interval; WMD, weighted mean difference. Figure S3. Forest plot comparing the salivary UA levels of periodontitis vs control before sensitivity analysis. There was a high heterogeneity among the studies (I2 = 88.6%, P < 0.001). Therefore, sensitivity analysis should be performed to find sources of heterogeneity. SMD, standardized mean difference. Figure S4. Sensitivity analysis of the relationship between periodontitis and controls of UA levels in saliva. A sensitivity analysis was performed to explore potential sources of heterogeneity. Statistical heterogeneity was decreased obviously, indicating that they were likely the source of heterogeneity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.760 | 0.023 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".